app.py

import streamlit as st 
import pandas as pd 
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier

st.title("""Iris App Classifier""")
st.sidebar.header('User input parameters')

def user_input_features():
    sepal_length = st.sidebar.slider('Sepal length',4.3,7.8,5.0)
    sepal_width = st.sidebar.slider('Sepal width',2.0,4.8,3.0)
    petal_length = st.sidebar.slider('petal length',1.0,6.9,1.3)
    petal_width = st.sidebar.slider('petal width',0.1,2.5,0.2)
    
    data = {'sepal_length':sepal_length,'sepal_width':sepal_width,
            'petal_length':petal_length,'petal_width':petal_width}
    
    features = pd.DataFrame(data,index=[0])
    return features

df = user_input_features()
st.write(df)

iris = datasets.load_iris()
X=iris.data
y=iris.target

clf = RandomForestClassifier()
clf.fit(X,y)

prediction = clf.predict(df)
prediction_proba = clf.predict_proba(df)

st.subheader('Class labels')
st.write(iris.target_names)

st.subheader('Prediction')
st.write(iris.target_names[prediction])

st.subheader('Prediction_Proba')
st.write(prediction_proba)